An unweighted graph treats every edge as equally important. In an adjacency matrix, edges are marked with 1 and missing edges with 0; weighted graphs use other values to distinguish edge importance. section 1.2.1
An unweighted graph can still be undirected: connections run both ways, so its adjacency matrix is symmetric. Whether edges are weighted is separate from whether the graph is directed. section 1.2.1
Sensor network
A sensor dataset uses 31 positions connected by a time-constant, undirected adjacency matrix with unweighted edges. The graph structure matters: connecting skeletal sensors merely because they are nearby can produce an incorrect representation. section 6.2.1 section 1.2.1
Table 1.1 The adjacency matrix for the simple graph in figure 1.1
|
A
|
B
|
C
|
D
|
E
|
|
|---|---|---|---|---|---|
|
A
|
0
|
1
|
0
|
0
|
1
|
|
B
|
1
|
0
|
1
|
1
|
1
|
|
C
|
0
|
1
|
0
|
1
|
0
|
|
D
|
0
|
1
|
1
|
0
|
1
|
|
E
|
1
|
1
|
0
|
1
|
0
|
The adjacency matrix shows a simple graph using binary entries to record which nodes are connected. section 1.2.1
